Revisiting the factor structure of the Short-Form McGill Pain Questionnaire-2 (SF-MPQ-2): Evidence for a bifactor model in individuals with Chiari malformation
Bibliographic record
Abstract
The Short-Form McGill Pain Questionnaire-2 (SF-MPQ-2; Dworkin et al., 2009) is intended to measure the multidimensional qualities of pain (i.e., continuous, intermittent, neuropathic, and affective) as well as total pain. Using structural equation modeling, we evaluated the fit of four competing measurement models of the SF-MPQ-2-an oblique 4-factor model, a 1-factor model, a higher-order model, and a bifactor model-in 552 adults diagnosed with Chiari malformation, a chronic health condition whose primary symptoms include head and neck pain. Results revealed the strongest support for the bifactor model, suggesting that SF-MPQ-2 item responses are due to both a general pain factor and a specific pain factor that is orthogonal to the general pain factor. Additional bifactor analyses of the SF-MPQ-2's model-based reliability and dimensionality revealed that most of the SF-MPQ-2's reliable variance is explained by a general pain factor, and that the instrument can be modeled unidimensionally and scored as a general pain measure. Results also indicated that the general and affective pain factors in the bifactor model uniquely predicted pain-related external criteria (e.g., depression, anxiety, and stress); however, the continuous, intermittent, and neuropathic factors did not.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".